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Middle Machine Learning Engineer

Bulgaria, Hungary, Poland

Pay
Salary not listed in the saved posting
Work setup
Unconfirmed
Employment
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Apply at Exadel Inc (Website)

What you’ll work on

Full posting
  • Design and implement end-to-end document intelligence pipelines on AWS

  • Develop and optimize ML models for document classification,segmentation, and field extraction

  • Build scalable data processing systems handling PDFs up to 2000 pages

From the employer’s posting
What You’ll Do Design and implement end-to-end document intelligence pipelines on AWS Develop and optimize ML models for document classification,segmentation, and field extraction
Design and implement end-to-end document intelligence pipelines on AWS Develop and optimize ML models for document classification,segmentation, and field extraction Build scalable data processing systems handling PDFs up to 2000 pages
Develop and optimize ML models for document classification,segmentation, and field extraction Build scalable data processing systems handling PDFs up to 2000 pages Collaborate with subject matter experts to create and refine requirements for extraction

What you’ll bring

All qualifications

Core experience

  • Experience in Python (native, Pandas, ScikitLearn, Tensorflow or Pytorch, PyStats, Pydantic)
  • Experience with GenAI for document intelligence, including prompt engineering, RAG (Retrieval Augmented Generation), multi-modal models (vision + text), and production deployment using AWS Bedrock or Azure OpenAI APIs
  • Experience with document processing tools (AWS Textract, Azure Document Intelligence, or similar OCR/layout detection systems)
  • Experience with AWS tools for ML Engineering and ML deployment (Sagemaker, Lambda, Cloudformation/CDK, Step Functions)
  • Experience in experiment design (power analysis and hypothesis testing)
  • Experience with PDF and Image processing libraries (e.g.
Qualification wording
Experience in Python (native, Pandas, ScikitLearn, Tensorflow or Pytorch, PyStats, Pydantic)
Experience with GenAI for document intelligence, including prompt engineering, RAG (Retrieval Augmented Generation), multi-modal models (vision + text), and production deployment using AWS Bedrock or Azure OpenAI APIs
Experience with document processing tools (AWS Textract, Azure Document Intelligence, or similar OCR/layout detection systems)
Experience with AWS tools for ML Engineering and ML deployment (Sagemaker, Lambda, Cloudformation/CDK, Step Functions)
Experience in experiment design (power analysis and hypothesis testing)
Experience with PDF and Image processing libraries (e.g. PyMuPDF, opnecv, pillow)

Tools in this posting

  • Python
  • SQL
  • AWS
  • Azure
  • Redshift
  • S3
  • SageMaker
  • pandas
  • PyTorch
  • TensorFlow
Source — Tool mentions in context
What You Bring - Experience in Python (native, Pandas, ScikitLearn, Tensorflow or Pytorch, PyStats, Pydantic) - Experience with AWS tools for ML Engineering and ML deployment (Sagemaker, Lambda, Cloudformation/CDK, Step Functions)
- Experience with AWS tools for ML Engineering and ML deployment (Sagemaker, Lambda, Cloudformation/CDK, Step Functions) - Advanced knowledge of SQL and Data Modeling - Experience with GenAI for document intelligence, including prompt engineering, RAG (Retrieval Augmented Generation), multi-modal models (vision + text), and production deployment using AWS Bedrock or Azure OpenAI APIs
What You’ll Do - Design and implement end-to-end document intelligence pipelines on AWS - Develop and optimize ML models for document classification,segmentation, and field extraction
- Experience in Python (native, Pandas, ScikitLearn, Tensorflow or Pytorch, PyStats, Pydantic) - Experience with AWS tools for ML Engineering and ML deployment (Sagemaker, Lambda, Cloudformation/CDK, Step Functions) - Advanced knowledge of SQL and Data Modeling
- Advanced knowledge of SQL and Data Modeling - Experience with GenAI for document intelligence, including prompt engineering, RAG (Retrieval Augmented Generation), multi-modal models (vision + text), and production deployment using AWS Bedrock or Azure OpenAI APIs - Experience in experiment design (power analysis and hypothesis testing)
Nice to have - Experience with document processing tools (AWS Textract, Azure Document Intelligence, or similar OCR/layout detection systems) - Experience with PDF and Image processing libraries (e.g. PyMuPDF, opnecv, pillow)
- Experience in Machine Learning/ Data Science (e.g., ML algorithm selection, feature engineering, model training, hyperparameter tuning, supervised and unsupervised learning implementation, building a model pipelines, using Machine Learning tools/libraries/frameworks) - Experience working with AWS big data technologies (Redshift, S3, EMR, Glue, etc.) English Level

About Exadel Inc (Website)

The leading provider of vehicle lifecycle solutions, with headquarters in Chicago, enables the companies that build, insure, and replace vehicles to power the next generation of transportation.

In the employer’s words · Read in context

Job description

View original posting ↗

Why Join Exadel

We’re an AI-first global tech company with 25+ years of engineering leadership, 2,000+ team members, and 500+ active projects powering Fortune 500 clients, including HBO, Microsoft, Google, and Starbucks.

From AI platforms to digital transformation, we partner with enterprise leaders to build what’s next.

What powers it all? Our people are ambitious, collaborative, and constantly evolving.

About the Client  

The leading provider of vehicle lifecycle solutions, with headquarters in Chicago, enables the companies that build, insure, and replace vehicles to power the next generation of transportation. Its platform delivers advanced mobile, artificial intelligence, and car technologies. It connects a network of 350+ insurance companies, 24,000+ repair facilities, hundreds of parts suppliers, and dozens of third-party data and service providers. The customer's collective solutions enhance productivity and help clients deliver better experiences for end consumers.

What You’ll Do

  • Design and implement end-to-end document intelligence pipelines on AWS
  • Develop and optimize ML models for document classification,segmentation, and field extraction
  • Build scalable data processing systems handling PDFs up to 2000 pages
  • Collaborate with subject matter experts to create and refine requirements for extraction
  • Own features from research through production deployment and monitoring
  • Establish evaluation frameworks and quality metrics for extraction accuracy

What You Bring

  • Experience in Python (native, Pandas, ScikitLearn, Tensorflow or Pytorch, PyStats, Pydantic)
  • Experience with AWS tools for ML Engineering and ML deployment (Sagemaker, Lambda, Cloudformation/CDK, Step Functions)
  • Advanced knowledge of SQL and Data Modeling
  • Experience with GenAI for document intelligence, including prompt engineering, RAG (Retrieval Augmented Generation), multi-modal models (vision + text), and production deployment using AWS Bedrock or Azure OpenAI APIs
  • Experience in experiment design (power analysis and hypothesis testing)
  • Proficiency in both written and verbal communication, required for a remote and largely asynchronous work environment
  • Demonstrated capacity to clearly and concisely communicate complex technical problems and propose iterative solutions
  • Experience owning a feature from concept to production, including proposal, discussion, and execution

Nice to have 

  • Experience with document processing tools (AWS Textract, Azure Document Intelligence, or similar OCR/layout detection systems)
  • Experience with PDF and Image processing libraries (e.g. PyMuPDF, opnecv, pillow)
  • Experience in Machine Learning/ Data Science (e.g., ML algorithm selection, feature engineering, model training, hyperparameter tuning, supervised and unsupervised learning implementation, building a model pipelines, using Machine Learning tools/libraries/frameworks)
  • Experience working with AWS big data technologies (Redshift, S3, EMR, Glue, etc.)

English Level

Upper-intermediate

Legal & Hiring Information

  • Exadel is proud to be an Equal Opportunity Employer committed to inclusion across minority, gender identity, sexual orientation, disability, age, and more.
  • Reasonable accommodations are available to enable individuals with disabilities to perform essential functions.
  • Please note: this job description is not exhaustive. Duties and responsibilities may evolve based on business needs.
  • Compensation details are shared with candidates at the early stage of the recruitment process.
  • The offer is not binding until a signed contract is in place.

Your Benefits at Exadel 

Exadel benefits vary by location and contract type. Your recruiter will fill you in on the details.

  • International projects
  • In-office, hybrid, or remote flexibility
  • Medical healthcare
  • Recognition program
  • Ongoing learning & reimbursement 
  • Well-being program
  • Team events & local benefits 
  • Sports compensation 
  • Referral bonuses 
  • Top-tier equipment provision

Exadel Culture

We lead with trust, respect, and purpose. We believe in open dialogue, creative freedom, and mentorship that helps you grow, lead, and make a real difference. Ours is a culture where ideas are challenged, voices are heard, and your impact matters.

Your next step

  • Have your CV and examples of relevant work ready.
  • Check the listed location, eligibility and core experience before starting.
  • Ask the employer about the salary range before committing time to the process.

Complete your application on job-boards.greenhouse.io. The employer’s form will show what is required.

Already applied? Track this application

Source & posting history

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Pay

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Location & working pattern

Bulgaria, Hungary, Poland

- Experience in experiment design (power analysis and hypothesis testing) - Proficiency in both written and verbal communication, required for a remote and largely asynchronous work environment - Demonstrated capacity to clearly and concisely communicate complex technical problems and propose iterative solutions
More source context
- International projects - In-office, hybrid, or remote flexibility - Medical healthcare
Work authorization

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Status in our records
Active
First seen by us
Oct 2, 2026
Recorded sightings
5
Last seen by us
Oct 8, 2026

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